Antenna Season Report Notebook¶

Josh Dillon, Last Revised January 2022

This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.

In [1]:
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
In [2]:
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
In [3]:
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "93"
csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
In [4]:
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))

Antenna 93 Report

In [5]:
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
In [6]:
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 29 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_
Found 29 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
In [7]:
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'

Load relevant info from summary CSVs¶

In [8]:
this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
            antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
        else:
            antnum = int(row['Ant'])
        if antnum != int(antenna):
            continue
        
        if np.issubdtype(type(row['Node']), np.integer):
            row['Node'] = str(row['Node'])
        if type(row['Node']) == str and row['Node'].isnumeric():
            row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break
In [9]:
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])

Table 1: Per-Night RTP Summary Info For This Atenna¶

This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.

In [10]:
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))

Antenna 93, Node N10:

Out[10]:
JDs A Priori Status Auto Metrics Flags Dead Fraction in Ant Metrics (Jee) Dead Fraction in Ant Metrics (Jnn) Crossed Fraction in Ant Metrics Flag Fraction Before Redcal Flagged By Redcal chi^2 Fraction ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score Average Dead Ant Metric (Jee) Average Dead Ant Metric (Jnn) Average Crossed Ant Metric Median chi^2 Per Antenna (Jee) Median chi^2 Per Antenna (Jnn)
2460012 digital_ok 100.00% 100.00% 100.00% 0.00% - - 10.829595 14.119577 10.964656 11.464057 6.353759 7.645385 4.244563 3.382623 0.0311 0.0250 0.0032 nan nan
2460011 digital_ok 100.00% 100.00% 100.00% 0.00% - - 12.214121 15.213806 14.600897 15.346447 13.159103 15.758880 3.463832 2.696231 0.0315 0.0250 0.0031 nan nan
2460010 digital_ok 100.00% 100.00% 100.00% 0.00% - - 13.142400 16.627284 11.737518 12.710213 9.128091 10.391299 3.295156 2.529069 0.0326 0.0250 0.0035 nan nan
2460009 digital_ok 100.00% 100.00% 100.00% 0.00% - - 12.214347 15.354810 13.077004 13.984490 7.227834 8.757203 2.741801 2.652610 0.0304 0.0250 0.0024 nan nan
2460008 digital_ok 100.00% 100.00% 100.00% 0.00% - - 14.778888 18.812773 14.325589 15.406158 6.568971 7.709592 5.030815 5.606525 0.0329 0.0251 0.0036 nan nan
2460007 digital_ok 100.00% 100.00% 100.00% 0.00% - - 10.968131 14.058676 11.206071 12.052463 5.844114 7.142346 3.437637 2.882310 0.0308 0.0250 0.0027 nan nan
2459999 digital_ok 0.00% 98.58% 99.16% 0.00% - - nan nan nan nan nan nan nan nan 0.2405 0.2381 0.1960 nan nan
2459998 digital_ok 100.00% 100.00% 100.00% 0.00% - - 9.288126 11.929962 9.583769 10.196922 7.810133 10.077726 2.374811 2.125250 0.0292 0.0249 0.0022 nan nan
2459997 digital_ok 100.00% 100.00% 100.00% 0.00% - - 10.202323 13.015217 10.152426 10.952879 7.617549 9.528469 4.368516 3.483700 0.0308 0.0250 0.0028 nan nan
2459996 digital_ok 100.00% 100.00% 100.00% 0.00% - - 11.334133 13.997242 12.733976 13.399696 7.199415 9.180272 1.784458 1.552713 0.0300 0.0250 0.0023 nan nan
2459995 digital_ok 100.00% 100.00% 100.00% 0.00% - - 11.590841 14.216545 11.839143 12.609271 7.842935 9.333909 1.557777 1.224150 0.0335 0.0252 0.0038 nan nan
2459994 digital_ok 100.00% 100.00% 100.00% 0.00% - - 11.057310 13.779566 10.224050 11.050328 7.670580 9.450352 1.425911 0.914928 0.0303 0.0250 0.0024 nan nan
2459993 digital_ok 100.00% 100.00% 100.00% 0.00% - - 12.235690 12.848061 9.509788 10.253649 10.026001 10.810482 1.794818 2.392972 0.0274 0.0260 0.0009 nan nan
2459991 digital_ok 100.00% 100.00% 100.00% 0.00% - - 13.091571 16.052847 10.077461 10.851289 9.048125 10.646472 1.480238 0.911130 0.0255 0.0248 0.0011 nan nan
2459990 digital_ok 100.00% 100.00% 100.00% 0.00% - - 10.618489 13.258239 9.873607 10.546135 8.955331 10.935985 1.477179 0.712331 0.0256 0.0249 0.0011 nan nan
2459989 digital_ok 100.00% 96.87% 97.08% 0.05% - - 245.535053 245.900237 inf inf 2802.456577 2762.394251 3139.582168 3159.607709 0.5434 0.5243 0.2792 nan nan
2459988 digital_ok 0.00% 0.00% 0.00% 0.00% - - 1.819035 -0.244004 2.374012 -1.138050 0.338435 -0.157531 2.268403 0.481028 0.6172 0.6379 0.3797 nan nan
2459987 digital_ok 0.00% 0.00% 0.00% 0.00% - - 1.499116 0.318381 2.337262 -0.942214 0.342533 0.531115 3.838632 -0.495089 0.6211 0.6421 0.3782 nan nan
2459986 digital_ok 0.00% 0.00% 0.00% 0.00% - - 2.545517 0.314741 2.589336 -1.168702 0.868358 0.587715 3.096269 -1.139558 0.6445 0.6662 0.3330 nan nan
2459985 digital_ok 0.00% 0.00% 0.00% 0.00% - - 2.247906 0.524603 2.474432 -1.010516 0.050620 0.700173 3.550482 1.201222 0.6227 0.6439 0.3839 nan nan
2459984 digital_ok 0.00% 0.00% 0.00% 0.00% - - 1.626251 -0.677902 2.403695 -1.041679 1.149476 0.218618 1.538518 -0.931408 0.6384 0.6619 0.3642 nan nan
2459983 digital_ok 0.00% 0.00% 0.00% 0.00% - - 1.892108 -0.724567 2.437070 -1.030237 0.817410 0.264800 3.379624 -0.557412 0.6508 0.6780 0.3156 nan nan
2459982 digital_ok 0.00% 0.00% 0.00% 0.00% - - -0.090915 -0.681351 2.020076 -0.561131 -0.092616 0.345225 1.867307 -0.440163 0.7001 0.7078 0.2869 nan nan
2459981 digital_ok 0.00% 0.00% 0.00% 0.00% - - 1.564592 -0.443294 2.437737 -1.256579 1.657941 0.046114 2.671656 0.049164 0.6198 0.6393 0.3821 nan nan
2459980 digital_ok 0.00% 0.00% 0.00% 0.00% - - 0.953432 -0.623196 2.161539 -1.046215 0.466375 0.020012 2.791273 -0.929814 0.6668 0.6813 0.3100 nan nan
2459979 digital_ok 0.00% 0.00% 0.00% 0.00% - - 1.692392 -0.366507 1.857719 -1.026741 0.686085 0.014313 1.888361 -0.358692 0.6164 0.6390 0.3850 nan nan
2459978 digital_ok 0.00% 0.00% 0.00% 0.00% - - 1.695139 -0.259433 2.046497 -1.138375 0.951096 0.003085 2.347261 -0.150069 0.6133 0.6345 0.3911 nan nan
2459977 digital_ok 0.00% 0.00% 0.00% 0.00% - - 1.844484 -0.292819 2.079915 -1.030622 2.046079 0.171515 3.223258 -0.186266 0.5828 0.6026 0.3522 nan nan
2459976 digital_ok 0.00% 0.00% 0.00% 0.00% - - 1.701281 -0.397274 2.238467 -1.163034 1.843390 0.540440 2.931997 -0.482447 0.6278 0.6469 0.3807 nan nan

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

In [11]:
htmls_to_display = []
for am_html in auto_metric_htmls:
    html_to_display = ''
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line:
            htmls_to_display.append(html_to_display)
            break

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.

In [12]:
for i, html_to_display in enumerate(htmls_to_display):
    if i == 100:
        break
    display(HTML(html_to_display))

Antenna 93: 2460012

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
93 N10 digital_ok nn Shape 14.119577 10.829595 14.119577 10.964656 11.464057 6.353759 7.645385 4.244563 3.382623

Antenna 93: 2460011

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
93 N10 digital_ok nn Temporal Variability 15.758880 12.214121 15.213806 14.600897 15.346447 13.159103 15.758880 3.463832 2.696231

Antenna 93: 2460010

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
93 N10 digital_ok nn Shape 16.627284 13.142400 16.627284 11.737518 12.710213 9.128091 10.391299 3.295156 2.529069

Antenna 93: 2460009

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
93 N10 digital_ok nn Shape 15.354810 12.214347 15.354810 13.077004 13.984490 7.227834 8.757203 2.741801 2.652610

Antenna 93: 2460008

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
93 N10 digital_ok nn Shape 18.812773 18.812773 14.778888 15.406158 14.325589 7.709592 6.568971 5.606525 5.030815

Antenna 93: 2460007

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
93 N10 digital_ok nn Shape 14.058676 10.968131 14.058676 11.206071 12.052463 5.844114 7.142346 3.437637 2.882310

Antenna 93: 2459999

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
93 N10 digital_ok nn Shape nan nan nan nan nan nan nan nan nan

Antenna 93: 2459998

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
93 N10 digital_ok nn Shape 11.929962 9.288126 11.929962 9.583769 10.196922 7.810133 10.077726 2.374811 2.125250

Antenna 93: 2459997

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
93 N10 digital_ok nn Shape 13.015217 10.202323 13.015217 10.152426 10.952879 7.617549 9.528469 4.368516 3.483700

Antenna 93: 2459996

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
93 N10 digital_ok nn Shape 13.997242 11.334133 13.997242 12.733976 13.399696 7.199415 9.180272 1.784458 1.552713

Antenna 93: 2459995

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
93 N10 digital_ok nn Shape 14.216545 11.590841 14.216545 11.839143 12.609271 7.842935 9.333909 1.557777 1.224150

Antenna 93: 2459994

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
93 N10 digital_ok nn Shape 13.779566 11.057310 13.779566 10.224050 11.050328 7.670580 9.450352 1.425911 0.914928

Antenna 93: 2459993

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
93 N10 digital_ok nn Shape 12.848061 12.235690 12.848061 9.509788 10.253649 10.026001 10.810482 1.794818 2.392972

Antenna 93: 2459991

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
93 N10 digital_ok nn Shape 16.052847 13.091571 16.052847 10.077461 10.851289 9.048125 10.646472 1.480238 0.911130

Antenna 93: 2459990

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
93 N10 digital_ok nn Shape 13.258239 13.258239 10.618489 10.546135 9.873607 10.935985 8.955331 0.712331 1.477179

Antenna 93: 2459989

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
93 N10 digital_ok nn Power inf 245.900237 245.535053 inf inf 2762.394251 2802.456577 3159.607709 3139.582168

Antenna 93: 2459988

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
93 N10 digital_ok ee Power 2.374012 -0.244004 1.819035 -1.138050 2.374012 -0.157531 0.338435 0.481028 2.268403

Antenna 93: 2459987

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
93 N10 digital_ok ee Temporal Discontinuties 3.838632 1.499116 0.318381 2.337262 -0.942214 0.342533 0.531115 3.838632 -0.495089

Antenna 93: 2459986

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
93 N10 digital_ok ee Temporal Discontinuties 3.096269 0.314741 2.545517 -1.168702 2.589336 0.587715 0.868358 -1.139558 3.096269

Antenna 93: 2459985

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
93 N10 digital_ok ee Temporal Discontinuties 3.550482 0.524603 2.247906 -1.010516 2.474432 0.700173 0.050620 1.201222 3.550482

Antenna 93: 2459984

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
93 N10 digital_ok ee Power 2.403695 1.626251 -0.677902 2.403695 -1.041679 1.149476 0.218618 1.538518 -0.931408

Antenna 93: 2459983

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
93 N10 digital_ok ee Temporal Discontinuties 3.379624 1.892108 -0.724567 2.437070 -1.030237 0.817410 0.264800 3.379624 -0.557412

Antenna 93: 2459982

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
93 N10 digital_ok ee Power 2.020076 -0.090915 -0.681351 2.020076 -0.561131 -0.092616 0.345225 1.867307 -0.440163

Antenna 93: 2459981

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
93 N10 digital_ok ee Temporal Discontinuties 2.671656 -0.443294 1.564592 -1.256579 2.437737 0.046114 1.657941 0.049164 2.671656

Antenna 93: 2459980

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
93 N10 digital_ok ee Temporal Discontinuties 2.791273 -0.623196 0.953432 -1.046215 2.161539 0.020012 0.466375 -0.929814 2.791273

Antenna 93: 2459979

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
93 N10 digital_ok ee Temporal Discontinuties 1.888361 1.692392 -0.366507 1.857719 -1.026741 0.686085 0.014313 1.888361 -0.358692

Antenna 93: 2459978

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
93 N10 digital_ok ee Temporal Discontinuties 2.347261 -0.259433 1.695139 -1.138375 2.046497 0.003085 0.951096 -0.150069 2.347261

Antenna 93: 2459977

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
93 N10 digital_ok ee Temporal Discontinuties 3.223258 1.844484 -0.292819 2.079915 -1.030622 2.046079 0.171515 3.223258 -0.186266

Antenna 93: 2459976

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
93 N10 digital_ok ee Temporal Discontinuties 2.931997 -0.397274 1.701281 -1.163034 2.238467 0.540440 1.843390 -0.482447 2.931997
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